A methodology for energy performance testing of smartphone applications
Bibliographic record
Abstract
Smartphones are becoming increasingly popular among users. They are equipped with an enormous number of applications, and these applications drain the smartphones' batteries. Moreover, battery capacity is significantly restricted due to constraints on size and weight of the device. It is important for smartphone applications to be energy efficient. Thus, a methodology to conduct energy performance testing is needed for two reasons: (i) evaluate the power consumption of a single application on a given device; (ii) compare the power consumption of different smartphones or platforms running the same application. In our earlier work “Selection and execution of user level test cases for energy cost evaluation of smartphones” (Proceedings of the 6th AST, 2011), we have developed a testing methodology that significantly reduces the number of test cases. In addition, we have introduced the concepts of primary and standalone test configurations. However, ordering of the executions of those two kinds of tests is non-trivial, and it was not studied in that paper. In this paper, we introduce a methodology to interleave the identification of those two kinds of test configurations in order to reduce the total number of configurations. We express the methodology in the form of a detailed flow chart that application developers can easily follow. We apply the methodology to a specific smartphone, namely HTC Nexus One smartphone in order to illustrate the process of this methodology. We have shown that the total number of test configurations obtained by the given methodology is the same as the number predicted by numerical expressions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".